Comparison
Design-in intelligence compared with intent data.
Intent data reports that people at a company are consuming content on a topic. It is built from publisher networks and review sites and sold as a score per company per topic.
It answers a different question from the one a component sale turns on.
What each one tells you.
Both are useful. They are not substitutes.
| Intent data | Design-in intelligence | |
|---|---|---|
| Unit | Company and topic | Product and part |
| Question answered | Is someone there reading about this? | Could this part serve this product, and is the design open? |
| Evidence | Aggregated content consumption | Documents, filings, teardowns, postings, each with a source |
| Can be checked by an engineer | No, the underlying events are not shown | Yes, every value links to the text it came from |
| Says what is inside the product | No | Where public evidence exists |
| Coverage | Broad, any company with web traffic | Narrow, one application class at a time |
Engineers choosing a component do not leave much of a trail.
Component selection happens in datasheets, distributor sites, reference designs and conversations with field engineers. Little of that passes through the networks intent data is built on, and topics are coarse. A score for a broad topic does not distinguish a team evaluating a mid-range device from a student reading a tutorial.
Prioritising outreach among companies already known to be a fit, and timing marketing to a company that is visibly researching a category.
Deciding whether a part fits a product, or finding products nobody at the company has started researching yet.
Using both.
- Fit and evidence decide which products are worth an engineer's time
- Intent scores can order the outreach among those
- Neither replaces the conversation
Design-in intelligence · Compared with technographics · Compared with lead databases · How signals are detected
Discuss what you already buy and what it misses